Simulation

So far nobody seems to have warned the OP about seeding.

Presumably Debbie wants 1000 different sets of samples, but as we all
know there are ways to get the same sequence (initial seed) every  
time.
  If there's a starting seed for one of the "generate a single giant
matrix" methods proposed, the whole matrix will be the same for a  
given
seed.
If rnorm is called 1000 times (hopefully w/ different random (oops)
seeds), the entire set of samples will be different.

and so on.

I really don't get this.  The OP wanted 1000 independent samples,
each of size 100.  Whether she does

set.seed(42)
M <- matrix(rnorm(100*1000),nrow=1000) # Each row is a sample.

or

L <- list()
set.seed(42)
for(i in 1:1000) L[[i]] <- rnorm(100) # Each list entry is a sample.

she gets this, i.e. the desired result.  Setting a seed serves to make
the results reproducible.  This works via either approach.  Making  
results
reproducible in this manner is advisable, but seed-setting is nothing  
that the OP
needs to be *warned* about.

	cheers,

		Rolf Turner

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